Acceptance of artificial intelligence clinical assistant decision support system to prevent and control venous thromboembolism among healthcare workers: an extend Unified Theory of Acceptance and Use of Technology Model
BackgroundVenous thromboembolism (VTE) is an important global health problem and the third most prevalent cardiovascular disorder. It has been proven that computerized tools were helpful in the prevention and control of VTE. However, studies that focused on the acceptance of computerized tools for V...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1475577/full |
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| Summary: | BackgroundVenous thromboembolism (VTE) is an important global health problem and the third most prevalent cardiovascular disorder. It has been proven that computerized tools were helpful in the prevention and control of VTE. However, studies that focused on the acceptance of computerized tools for VTE prevention among healthcare workers were limited.ObjectiveThis study aims to explore what factors are influencing healthcare workers’ acceptance of the Artificial Intelligence Clinical Assistant Decision Support System (AI-CDSS) for VTE prevention based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT).MethodsWe conducted a cross-sectional survey among healthcare workers in three grade-A tertiary hospitals in Shanxi, China. Statistically, the hypothesized model was evaluated by AMOS structural equation modeling.Results510 (72.86%) valid surveys were collected in total. The results showed that performance expectancy (β = 0.45, P < 0.001), effort expectancy (β = 0.21, P < 0.001), and top management support (β = 0.30, P < 0.001) positively influenced healthcare workers’ intention. Top management support was an antecedent of performance expectancy (β = 0.41 , P < 0.001), social influence (β = 0.57, P < 0.001), effort expectancy (β = 0.61, P < 0.001), and information quality (β = 0.59, P < 0.001). In addition, Social influence positively influenced performance expectancy (β = 0.52, P < 0.001), and information quality positively influenced system quality (β = 0.65, P < 0.001). Social influence did not influence nurses’ behavioral intention (β = 0.06, p = 0.376), but negatively influenced clinicians’ behavioral intention in the model (β = −0.19, P < 0.001). System quality positively influenced nurses’ behavioral intention; (β = 0.16, P < 0.001), and information quality positively influenced clinicians’ behavioral intention (β = 0.15, p = 0.025).ConclusionWith this model explaining 76.3% variance of the behavioral intention variable, this study could be useful as a reference for hospital administrators to evaluate future developments and facilitate the implementation of AI-CDSS for VTE prevention. |
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| ISSN: | 2296-858X |